吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

基于最小信息准则和BP算法的网络入侵检测

郭其标, 李秉键   

  1. 嘉应学院 计算机学院, 广东 梅州 514015
  • 收稿日期:2014-12-08 出版日期:2015-07-26 发布日期:2015-07-27
  • 通讯作者: 郭其标 E-mail:guoqb@jyu.edu.cn

Network Intrusion Detection Based on AkaikeInformation Criterion and BP Algorithm

GUO Qibiao, LI Bingjian   

  1. School of Computer, Jiaying University, Meizhou 514015, Guangdong Province, China
  • Received:2014-12-08 Online:2015-07-26 Published:2015-07-27
  • Contact: GUO Qibiao E-mail:guoqb@jyu.edu.cn

摘要:

针对网络入侵检测中BP(back propagation)神经网络只能根据经验公式确定隐层神经元个数的问题, 提出一种利用统计学中最小信息准则计算最优的网络结构, 并推导出网络结构的评价公式. 仿真实验结果表明, 经过结构优化后的BP神经网络对于网络入侵检测的准确率明显提高, 平均分类准确率达到90%以上, 算法的整体性能表现优良.

关键词: 网络入侵检测, 最小信息准则, BP算法, 数据挖掘

Abstract:

For the problem that the number of hidden layer neurons of the BP neural network can only be determined by empirical formula, the authors used the Akaike information criterion in statistics to calculate the optimal network structure. Simulation results show that the accuracy of network intrusion detection is significantly improved by the BP neural network after structural optimization, with an average classification accuracy rate of more than 90%. The overall performance of this algorithm is good.

Key words: network intrusion, Akaike information criterion, BP algorithm, data mining

中图分类号: 

  • TP393.08